Gastos Casa
30 December, 2024
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 28/10/2024 | Diosi | 15000 | Andrés | NA |
| 29/10/2024 | Electricidad | 50227 | Andrés | NA |
| 31/10/2024 | Electrodomésticos/mantención casa | 13264 | Andrés | lona sombreadora |
| 31/10/2024 | Agua | 12000 | Andrés | NA |
| 4/11/2024 | Comida | 64915 | Tami | Supermercado |
| 7/11/2024 | Comida | 34229 | Andrés | piwen |
| 8/11/2024 | Assistcard viaje | 44836 | Tami | Assistcard viaje |
| 9/11/2024 | Comida | 35916 | Tami | Supermercado |
| 15/11/2024 | Comida | 12576 | Tami | Supermercado |
| 17/11/2024 | Comida | 91203 | Tami | Supermercado |
| 18/11/2024 | Diosi | 49500 | Tami | Veterinaria |
| 23/11/2024 | Comida | 50929 | Tami | Supermercado |
| 26/11/2024 | Enceres | 12990 | Tami | Confort 48 rollos |
| 27/11/2024 | Transporte | 21780 | Tami | Transvip aeropuerto |
| 27/11/2024 | Electricidad | 45000 | Andrés | Eléctrico |
| 30/11/2024 | Comida | 62899 | Tami | Supermercado |
| 11/12/2024 | Comida | 17738 | Andrés | compra lider despues viaje |
| 12/12/2024 | Comida | 55616 | Tami | Supermercado |
| 14/12/2024 | Diosi | 16276 | Andrés | antiparasitario |
| 15/12/2024 | Comida | 23773 | Tami | Supermercado |
| 18/12/2024 | Comida | 41554 | Tami | Supermercado |
| 18/12/2024 | VTR | 22000 | Andrés | NA |
| 20/12/2024 | Plata basureros | 10000 | Tami | NA |
| 3/12/2024 | Agua | 15828 | Andrés | PAC AGUAS ANDIN 000000005687837 |
| 22/12/2024 | Comida | 46608 | Tami | Supermercado |
| 10/12/2024 | Otros | 47484 | Andrés | viaje brasil |
| 29/12/2024 | Electricidad | 27417 | Andrés | NA |
| 29/12/2024 | Comida | 83284 | Tami | Supermercado |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7 # Weekly data
num_periods <- length(tsData_gastos$x) # Total number of periods in your time series
# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)
# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
day = dates,
Actual = as.numeric(tsData_gastos$x),
Seasonal = as.numeric(tsData_gastos$seasonal),
Trend = as.numeric(tsData_gastos$trend),
Random = as.numeric(tsData_gastos$random)
)
tsData_gastos_long <- tsData_gastos_df %>%
pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"),
names_to = "Component", values_to = "Value")
# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
theme(strip.text = element_text(size = 12))
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 9.7849e+08 2 4.8787 0.0078 **
## lag_depvar 2.6090e+11 1 2601.6691 <2e-16 ***
## Residuals 7.9724e+10 795
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 -2001.493 16459.17 0.1576445
## 2-0 31580.425 23261.351 39899.50 0.0000000
## 2-1 24351.587 19521.410 29181.76 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
## 38 38443.00 1 34770.57
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## 300 89765.00 2 84485.00
## 301 87702.86 2 89765.00
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## 304 57248.43 2 85982.43
## 305 52968.43 2 57248.43
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## 619 59609.00 2 67061.57
## 620 85054.00 2 59609.00
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## 622 59242.29 2 68023.29
## 623 61535.14 2 59242.29
## 624 56215.86 2 61535.14
## 625 45152.29 2 56215.86
## 626 57409.57 2 45152.29
## 627 35151.43 2 57409.57
## 628 34991.43 2 35151.43
## 629 45944.71 2 34991.43
## 630 57944.71 2 45944.71
## 631 55706.29 2 57944.71
## 632 88593.71 2 55706.29
## 633 77359.43 2 88593.71
## 634 79878.71 2 77359.43
## 635 81753.00 2 79878.71
## 636 75716.00 2 81753.00
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## 638 63528.57 2 67381.43
## 639 49682.86 2 63528.57
## 640 47815.00 2 49682.86
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## 642 44808.71 2 46546.14
## 643 42959.57 2 44808.71
## 644 46023.86 2 42959.57
## 645 51309.57 2 46023.86
## 646 68447.29 2 51309.57
## 647 84959.29 2 68447.29
## 648 81666.29 2 84959.29
## 649 82700.86 2 81666.29
## 650 89422.14 2 82700.86
## 651 104812.71 2 89422.14
## 652 98812.71 2 104812.71
## 653 64779.86 2 98812.71
## 654 61862.86 2 64779.86
## 655 58376.43 2 61862.86
## 656 59503.57 2 58376.43
## 657 55429.43 2 59503.57
## 658 44454.57 2 55429.43
## 659 47184.00 2 44454.57
## 660 52126.71 2 47184.00
## 661 51202.00 2 52126.71
## 662 64437.14 2 51202.00
## 663 64297.14 2 64437.14
## 664 64628.57 2 64297.14
## 665 51413.14 2 64628.57
## 666 52969.43 2 51413.14
## 667 54135.29 2 52969.43
## 668 48799.43 2 54135.29
## 669 41907.86 2 48799.43
## 670 45382.00 2 41907.86
## 671 42633.29 2 45382.00
## 672 46624.71 2 42633.29
## 673 44051.86 2 46624.71
## 674 35852.86 2 44051.86
## 675 29737.71 2 35852.86
## 676 29734.86 2 29737.71
## 677 32881.71 2 29734.86
## 678 38298.57 2 32881.71
## 679 40886.14 2 38298.57
## 680 38601.86 2 40886.14
## 681 38628.86 2 38601.86
## 682 39142.57 2 38628.86
## 683 32666.14 2 39142.57
## 684 39911.57 2 32666.14
## 685 39336.29 2 39911.57
## 686 39678.86 2 39336.29
## 687 41963.14 2 39678.86
## 688 54220.57 2 41963.14
## 689 63901.86 2 54220.57
## 690 73116.00 2 63901.86
## 691 60863.86 2 73116.00
## 692 56293.86 2 60863.86
## 693 52725.00 2 56293.86
## 694 58625.00 2 52725.00
## 695 47513.00 2 58625.00
## 696 40300.14 2 47513.00
## 697 33312.43 2 40300.14
## 698 29556.71 2 33312.43
## 699 27816.71 2 29556.71
## 700 34120.29 2 27816.71
## 701 32132.57 2 34120.29
## 702 32902.57 2 32132.57
## 703 39694.14 2 32902.57
## 704 72501.29 2 39694.14
## 705 79551.14 2 72501.29
## 706 99637.71 2 79551.14
## 707 95424.29 2 99637.71
## 708 98395.14 2 95424.29
## 709 115594.71 2 98395.14
## 710 114267.57 2 115594.71
## 711 88353.29 2 114267.57
## 712 88750.86 2 88353.29
## 713 78835.71 2 88750.86
## 714 75519.14 2 78835.71
## 715 73202.86 2 75519.14
## 716 53433.29 2 73202.86
## 717 48165.71 2 53433.29
## 718 52163.14 2 48165.71
## 719 49306.86 2 52163.14
## 720 36846.86 2 49306.86
## 721 43220.57 2 36846.86
## 722 38952.29 2 43220.57
## 723 41522.29 2 38952.29
## 724 39090.00 2 41522.29
## 725 28452.57 2 39090.00
## 726 32975.00 2 28452.57
## 727 33690.71 2 32975.00
## 728 26405.29 2 33690.71
## 729 47087.43 2 26405.29
## 730 49660.29 2 47087.43
## 731 47409.71 2 49660.29
## 732 53881.71 2 47409.71
## 733 45189.57 2 53881.71
## 734 45503.86 2 45189.57
## 735 54640.14 2 45503.86
## 736 39131.29 2 54640.14
## 737 35024.14 2 39131.29
## 738 44755.43 2 35024.14
## 739 41063.29 2 44755.43
## 740 42783.29 2 41063.29
## 741 45952.57 2 42783.29
## 742 44937.43 2 45952.57
## 743 40838.43 2 44937.43
## 744 48838.43 2 40838.43
## 745 43139.14 2 48838.43
## 746 67134.29 2 43139.14
## 747 73224.29 2 67134.29
## 748 68770.71 2 73224.29
## 749 59539.29 2 68770.71
## 750 82179.86 2 59539.29
## 751 74252.14 2 82179.86
## 752 73015.00 2 74252.14
## 753 56116.43 2 73015.00
## 754 111885.00 2 56116.43
## 755 131425.14 2 111885.00
## 756 136678.00 2 131425.14
## 757 115531.29 2 136678.00
## 758 118310.86 2 115531.29
## 759 117449.43 2 118310.86
## 760 115193.57 2 117449.43
## 761 61025.43 2 115193.57
## 762 43913.86 2 61025.43
## 763 46099.29 2 43913.86
## 764 44524.86 2 46099.29
## 765 42208.71 2 44524.86
## 766 166486.57 2 42208.71
## 767 171565.29 2 166486.57
## 768 200415.71 2 171565.29
## 769 204498.14 2 200415.71
## 770 197558.86 2 204498.14
## 771 195266.57 2 197558.86
## 772 203144.29 2 195266.57
## 773 85493.71 2 203144.29
## 774 74721.57 2 85493.71
## 775 36232.14 2 74721.57
## 776 40161.71 2 36232.14
## 777 40629.86 2 40161.71
## 778 45663.71 2 40629.86
## 779 39252.29 2 45663.71
## 780 39618.57 2 39252.29
## 781 39438.43 2 39618.57
## 782 44650.71 2 39438.43
## 783 38626.71 2 44650.71
## 784 38280.43 2 38626.71
## 785 44134.14 2 38280.43
## 786 47596.43 2 44134.14
## 787 45598.43 2 47596.43
## 788 42564.29 2 45598.43
## 789 45699.14 2 42564.29
## 790 49553.86 2 45699.14
## 791 50018.43 2 49553.86
## 792 43772.86 2 50018.43
## 793 39235.43 2 43772.86
## 794 39905.00 2 39235.43
## 795 40374.43 2 39905.00
## 796 34230.57 2 40374.43
## 797 34324.14 2 34230.57
## 798 33491.57 2 34324.14
## 799 33366.43 2 33491.57
## 800 46646.86 2 33366.43
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 643 53814.69 22643.903
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57 35151.43 34991.43 45944.71 57944.71 55706.29
## [631] 88593.71 77359.43 79878.71 81753.00 75716.00 67381.43 63528.57
## [638] 49682.86 47815.00 46546.14 44808.71 42959.57 46023.86 51309.57
## [645] 68447.29 84959.29 81666.29 82700.86 89422.14 104812.71 98812.71
## [652] 64779.86 61862.86 58376.43 59503.57 55429.43 44454.57 47184.00
## [659] 52126.71 51202.00 64437.14 64297.14 64628.57 51413.14 52969.43
## [666] 54135.29 48799.43 41907.86 45382.00 42633.29 46624.71 44051.86
## [673] 35852.86 29737.71 29734.86 32881.71 38298.57 40886.14 38601.86
## [680] 38628.86 39142.57 32666.14 39911.57 39336.29 39678.86 41963.14
## [687] 54220.57 63901.86 73116.00 60863.86 56293.86 52725.00 58625.00
## [694] 47513.00 40300.14 33312.43 29556.71 27816.71 34120.29 32132.57
## [701] 32902.57 39694.14 72501.29 79551.14 99637.71 95424.29 98395.14
## [708] 115594.71 114267.57 88353.29 88750.86 78835.71 75519.14 73202.86
## [715] 53433.29 48165.71 52163.14 49306.86 36846.86 43220.57 38952.29
## [722] 41522.29 39090.00 28452.57 32975.00 33690.71 26405.29 47087.43
## [729] 49660.29 47409.71 53881.71 45189.57 45503.86 54640.14 39131.29
## [736] 35024.14 44755.43 41063.29 42783.29 45952.57 44937.43 40838.43
## [743] 48838.43 43139.14 67134.29 73224.29 68770.71 59539.29 82179.86
## [750] 74252.14 73015.00 56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57 61025.43 43913.86 46099.29 44524.86
## [764] 42208.71 166486.57 171565.29 200415.71 204498.14 197558.86 195266.57
## [771] 203144.29 85493.71 74721.57 36232.14 40161.71 40629.86 45663.71
## [778] 39252.29 39618.57 39438.43 44650.71 38626.71 38280.43 44134.14
## [785] 47596.43 45598.43 42564.29 45699.14 49553.86 50018.43 43772.86
## [792] 39235.43 39905.00 40374.43 34230.57 34324.14 33491.57 33366.43
## [799] 46646.86
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [778] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6 7
## 2026.81908 4043.77129 -541.41124 2435.17663 -2976.23516 516.34589
## 8 9 10 11 12 13
## -5659.37729 -1183.71322 -3961.61328 -409.16038 -4932.15261 -1596.56218
## 14 15 16 17 18 19
## -886.97098 389.21479 -3233.83166 -366.12966 -2119.96989 6615.29673
## 20 21 22 23 24 25
## -1529.60633 -1207.24952 1477.48397 -1187.80132 234.53368 1693.84823
## 26 27 28 29 30 31
## -7106.15548 953.36122 8195.53770 408.84587 -23.35066 -2409.33804
## 32 33 34 35 36 37
## 1571.32912 4565.42789 1113.71268 2377.74698 -1883.90636 4596.08354
## 38 39 40 41 42 43
## 4296.77189 -2287.60263 -2988.64971 -1112.28110 -10741.79711 7304.13797
## 44 45 46 47 48 49
## 2559.81326 1365.41129 8101.92907 671.85214 6515.25961 6693.36062
## 50 51 52 53 54 55
## -5910.20925 -4811.52347 -5067.04549 -7927.85189 6141.58763 -4075.03282
## 56 57 58 59 60 61
## -4887.25318 3868.82892 893.68758 -28.32985 145.50618 -4993.83641
## 62 63 64 65 66 67
## 18136.03934 3623.10225 -3666.61512 5912.59821 7324.55534 14611.55952
## 68 69 70 71 72 73
## 1648.77142 -13253.82403 -1323.24421 4630.49716 -4918.17525 -4413.14938
## 74 75 76 77 78 79
## -10498.62533 2481.00328 -5390.74418 1079.52522 -6853.91044 567.69778
## 80 81 82 83 84 85
## -2337.10872 -2675.11774 -3911.51124 -513.94531 2336.06730 3778.08602
## 86 87 88 89 90 91
## 484.85248 -478.38450 202.60773 4306.80843 -1165.06608 1150.67328
## 92 93 94 95 96 97
## -2066.35237 -1042.99932 180.17856 276.74243 -7482.77710 2403.77750
## 98 99 100 101 102 103
## -8595.44759 -2921.70472 -4018.45547 -1712.20218 -1237.11700 3204.17195
## 104 105 106 107 108 109
## -2326.36441 2611.20685 -1147.19065 981.96407 2595.70972 -3150.68658
## 110 111 112 113 114 115
## -4715.19280 -836.42401 1916.90090 11702.45142 -1252.02284 2662.09378
## 116 117 118 119 120 121
## 4253.30700 3488.12284 -1117.75441 -4730.23063 -3729.25992 2320.79068
## 122 123 124 125 126 127
## -1735.10825 1340.87408 8856.72024 832.81673 116.76741 -2533.36877
## 128 129 130 131 132 133
## 2648.23343 7042.69633 993.55028 -8517.36463 1745.98373 4130.14324
## 134 135 136 137 138 139
## -3174.70771 -1424.48227 -856.03337 -3880.53401 1188.30654 -492.59842
## 140 141 142 143 144 145
## -2910.34808 1725.30136 -1877.36509 -7823.29376 2056.21742 -3468.08391
## 146 147 148 149 150 151
## 2117.46820 -247.31649 1032.21170 -352.85523 1358.28447 1189.89409
## 152 153 154 155 156 157
## 3357.61383 -4865.88444 -1170.93741 -3231.02920 5965.62534 9745.61040
## 158 159 160 161 162 163
## -3652.14446 -4995.55280 3391.50032 -26.13523 2472.83663 -6140.01334
## 164 165 166 167 168 169
## -6969.10070 3943.28996 17169.04610 3367.37771 -664.49007 -2709.21025
## 170 171 172 173 174 175
## -1361.14984 3336.43818 -488.54670 -8334.17121 2620.87311 4076.15413
## 176 177 178 179 180 181
## 367.66665 8491.97681 -9523.02996 -3726.10658 -10992.29073 -11468.19494
## 182 183 184 185 186 187
## 1024.94106 9075.56231 -1670.29980 5689.03039 6301.10433 12888.51085
## 188 189 190 191 192 193
## 8131.95488 -4379.04818 2159.96987 10059.73103 -1973.63251 -2766.80332
## 194 195 196 197 198 199
## -10593.76556 -6650.52152 961.39202 -5508.53388 -10059.23279 5141.77852
## 200 201 202 203 204 205
## -3324.61603 -1962.99233 -1052.68311 6245.32857 9613.80112 283.28160
## 206 207 208 209 210 211
## 2628.82558 2796.09177 5476.93303 12514.94829 -6033.00110 -11620.69514
## 212 213 214 215 216 217
## -5958.19390 -10863.22852 -5324.61977 1287.86379 -13255.99916 16173.23514
## 218 219 220 221 222 223
## 7546.98261 1244.38032 26400.86719 12170.86370 6954.42142 13637.29730
## 224 225 226 227 228 229
## -4327.53214 -2130.98067 3403.69274 -12.28415 2384.40244 8647.11011
## 230 231 232 233 234 235
## 5462.59854 -2274.18365 -2178.68212 9089.54148 -11859.66639 -7597.50640
## 236 237 238 239 240 241
## -8832.81102 -10370.23175 2833.07817 1097.30786 -8556.75570 -9229.85807
## 242 243 244 245 246 247
## 8873.43254 -8014.73533 2254.84281 -10544.22267 -4274.49099 1206.44792
## 248 249 250 251 252 253
## 776.63052 -12550.46357 3435.32215 1837.29353 3975.05559 1881.90141
## 254 255 256 257 258 259
## -1422.55265 10877.56973 20586.03896 2843.12328 -4628.99767 3769.62216
## 260 261 262 263 264 265
## -2041.23238 3400.74864 -5193.91620 -11217.04279 -5018.39764 -798.32064
## 266 267 268 269 270 271
## -5464.85017 8514.27352 -4573.25519 3907.76310 -2403.02885 4140.05795
## 272 273 274 275 276 277
## 403.64787 6995.34136 -1741.92628 11701.29294 -4944.89932 1382.54703
## 278 279 280 281 282 283
## -717.75055 7510.38881 -5420.50757 -3072.23282 -11589.12655 -2956.40720
## 284 285 286 287 288 289
## 18376.11017 7440.79489 2368.66123 -997.73420 544.98108 6039.39042
## 290 291 292 293 294 295
## 6506.78675 -19164.77081 -11454.77869 -8393.28418 9422.47459 2792.00887
## 296 297 298 299 300 301
## -1470.41186 27115.50705 9677.95061 4485.25898 9096.30097 2412.72106
## 302 303 304 305 306 307
## -1471.34269 7476.65211 -24730.92801 -3860.85750 -481.26405 -7268.98571
## 308 309 310 311 312 313
## -4241.47954 2679.37203 -9456.71492 -3457.25162 -8402.42491 1378.93022
## 314 315 316 317 318 319
## -3350.14176 1856.11330 -4290.11614 27247.81540 -1060.12932 2961.48971
## 320 321 322 323 324 325
## 10490.44815 5209.56339 31987.28388 4598.70192 -21443.79262 1397.09490
## 326 327 328 329 330 331
## 721.16587 -6846.09019 -2076.36837 -33594.11462 737.53402 -2454.09702
## 332 333 334 335 336 337
## -238.76968 -3317.81391 3944.35575 -602.65532 -7120.65115 -3258.20619
## 338 339 340 341 342 343
## -2326.08643 -7811.57233 3745.92183 -1505.39367 -1874.24322 -1130.62731
## 344 345 346 347 348 349
## 36.27839 332.56125 -1777.29213 -9605.03619 -13333.26657 2235.70788
## 350 351 352 353 354 355
## -4423.31448 -3751.37032 -6069.18902 1675.19647 1284.82797 2632.87255
## 356 357 358 359 360 361
## -3912.66101 -655.38248 529.84986 6852.89787 73.37948 -247.43492
## 362 363 364 365 366 367
## 2370.21653 -2978.65805 -1092.98111 -8955.86162 -4795.57011 -6363.25029
## 368 369 370 371 372 373
## -5075.54131 -7362.70636 4930.12904 245.85837 6981.39583 -7820.68604
## 374 375 376 377 378 379
## -2413.28994 -3533.79627 -2603.07268 -12589.21282 1824.84680 -10736.47868
## 380 381 382 383 384 385
## 5634.70405 9228.00031 2956.10229 -2592.95502 1417.08294 6542.03383
## 386 387 388 389 390 391
## 11168.16725 -6107.46669 -5637.92297 -405.77087 8313.92080 1518.88941
## 392 393 394 395 396 397
## 10918.04777 -10239.20669 2470.40194 396.84231 246.74723 -968.23482
## 398 399 400 401 402 403
## -869.85267 -14787.25252 8308.94453 -1438.01622 -1619.88336 6743.93874
## 404 405 406 407 408 409
## -8207.19858 -1523.44646 -2748.80326 -6020.72833 -3027.95291 -4072.76716
## 410 411 412 413 414 415
## -8893.64606 6036.51546 1498.06252 -7533.09925 -7817.67143 14128.57322
## 416 417 418 419 420 421
## 3628.74319 4274.43812 -8283.80400 -4946.95759 -2778.65131 2653.72550
## 422 423 424 425 426 427
## -14197.46337 -2903.48007 -9204.57427 2947.23240 6879.52646 6425.30680
## 428 429 430 431 432 433
## -4183.44776 -4298.15424 -4880.99092 -1928.03288 -5848.14090 -6739.63276
## 434 435 436 437 438 439
## -6036.73315 -1460.96063 -923.05885 -5060.42078 2509.66521 4737.77404
## 440 441 442 443 444 445
## -5198.01660 -2280.45114 1455.85475 -3976.58198 2708.71953 -6730.05922
## 446 447 448 449 450 451
## -12233.59613 -4578.78414 9587.36379 -2156.87119 4631.65661 -6026.56846
## 452 453 454 455 456 457
## -1253.67306 251.11085 2883.92736 -12433.58829 3263.87023 -6834.56927
## 458 459 460 461 462 463
## 6416.35674 2861.83768 2334.74092 -4035.07559 1921.65770 -193.10735
## 464 465 466 467 468 469
## 1604.95486 -720.73234 3152.67420 -2855.56444 5604.08943 -7173.23440
## 470 471 472 473 474 475
## -3154.63435 -2379.33639 -4827.07444 2855.35234 7635.68201 -6222.64340
## 476 477 478 479 480 481
## 1311.45018 -6360.91979 -2995.01040 1872.38753 -13084.81658 -9848.48257
## 482 483 484 485 486 487
## -1257.10148 -43.04107 -1039.41701 -1426.66697 -9674.46791 11042.08491
## 488 489 490 491 492 493
## 6111.25219 7256.96273 -5641.32615 5190.67878 9086.92291 5801.20513
## 494 495 496 497 498 499
## -13751.57170 -10766.71804 -3586.65731 -1237.66693 -655.80335 -7760.12129
## 500 501 502 503 504 505
## 510.50724 4175.90265 5368.03653 487.44483 -97.07986 -7417.86431
## 506 507 508 509 510 511
## 426.84803 -5198.24805 1704.47107 -1439.22675 -8298.39045 -706.70028
## 512 513 514 515 516 517
## -2784.33841 -692.11712 1222.32410 -9619.56990 -7849.48190 24229.32823
## 518 519 520 521 522 523
## 9634.04546 5638.85992 -5600.97468 2566.64182 16777.18875 11151.18792
## 524 525 526 527 528 529
## -24516.20014 -5288.98451 -3933.04685 4392.23312 -560.64912 -11303.70959
## 530 531 532 533 534 535
## 4246.92039 13738.68649 -5219.56178 4160.09181 5320.80104 -2049.08431
## 536 537 538 539 540 541
## -4784.50525 -7289.61768 -2277.54497 8155.53454 -83.15276 -8350.21785
## 542 543 544 545 546 547
## 1650.17168 -776.32472 191.64000 -11208.43332 -11185.05550 1965.91684
## 548 549 550 551 552 553
## 6907.15333 -1458.05416 698.24124 -7868.46884 8451.50469 743.14291
## 554 555 556 557 558 559
## -12115.03848 9048.43177 8489.60192 -115.84792 4639.04638 -3811.39600
## 560 561 562 563 564 565
## 13892.94068 21213.74071 -6851.65544 -10015.08592 6503.10236 -72.04241
## 566 567 568 569 570 571
## 3165.72055 -7673.70274 -17555.80833 6510.21593 6247.65724 1684.81738
## 572 573 574 575 576 577
## 2875.69886 1536.76647 -2401.13156 14497.71019 -9938.23051 -6476.25885
## 578 579 580 581 582 583
## 8515.19688 2620.29275 -6792.39014 7300.97584 -4042.26234 -2992.91971
## 584 585 586 587 588 589
## 15502.94755 -14774.88763 8232.83096 -164.95729 -6442.12093 -947.34354
## 590 591 592 593 594 595
## 64.87295 -10839.85591 1664.37603 -7289.42395 2955.17885 8732.83814
## 596 597 598 599 600 601
## -7681.91008 5707.18555 2558.61007 6665.83351 -3412.43279 5947.58564
## 602 603 604 605 606 607
## -8527.52131 2072.62349 1077.62703 2942.04586 1286.46957 185.57643
## 608 609 610 611 612 613
## -6019.70906 7898.21221 -1398.07726 -2777.34785 -3639.53271 -8394.84573
## 614 615 616 617 618 619
## 11832.52536 4749.70700 -9522.09348 11468.07003 5836.29340 -5809.45691
## 620 621 622 623 624 625
## 26158.57145 -13143.44318 -7017.93294 2960.68912 -4365.47064 -10773.21933
## 626 627 628 629 630 631
## 11167.70255 -21818.90136 -2496.96838 8596.36088 11009.25474 -1732.43987
## 632 633 634 635 636 637
## 33114.22294 -6905.51373 5446.82962 5116.05466 -2561.45547 -5612.00881
## 638 639 640 641 642 643
## -2169.84754 -12643.26319 -2392.35196 -2026.32573 -2653.15895 -2981.57894
## 644 645 646 647 648 649
## 1701.21006 4304.84034 16816.11601 18327.95166 582.45760 4499.30028
## 650 651 652 653 654 655
## 10315.05441 19822.67152 351.73267 -28429.49145 -1558.47875 -2491.73841
## 656 657 658 659 660 661
## 1686.97835 -3373.72128 -10782.59457 1552.82102 4106.54244 -1144.39210
## 662 663 664 665 666 667
## 12900.12744 1175.77524 1629.74191 -11875.77686 1247.60568 1051.28925
## 668 669 670 671 672 673
## -5305.01020 -7526.25437 1979.88916 -3809.64568 2587.65600 -3478.78748
## 674 675 676 677 678 679
## -9425.83723 -8364.62357 -3015.06636 134.29156 2796.79223 643.13935
## 680 681 682 683 684 685
## -3905.97562 -1879.60391 -1389.52197 -8315.59036 4598.47590 -2318.53180
## 686 687 688 689 690 691
## -1472.42880 512.01368 10770.07054 9722.77016 10463.15307 -9853.87260
## 692 693 694 695 696 697
## -3699.91294 -3268.77627 5754.94506 -10521.16073 -8007.99353 -8682.49470
## 698 699 700 701 702 703
## -6322.05714 -4774.78495 3051.76005 -4453.29488 -1943.50388 4174.10798
## 704 705 706 707 708 709
## 31036.77738 9371.45542 23287.48310 1492.83758 8151.59149 22750.85434
## 710 711 712 713 714 715
## 6369.40529 -18383.26922 4696.35528 -5566.77078 -204.89361 381.72334
## 716 717 718 719 720 721
## -17360.46766 -5324.28342 3283.70382 -3071.41987 -13031.39248 4248.21304
## 722 723 724 725 726 727
## -5598.80741 707.10429 -3974.63090 -12483.14748 1349.92627 -1892.71531
## 728 729 730 731 732 733
## -9804.58867 17254.28705 1724.64015 -2777.88153 5663.98101 -8692.92333
## 734 735 736 737 738 739
## -770.64688 8090.55329 -15415.04046 -5947.71229 7378.44127 -4831.22515
## 740 741 742 743 744 745
## 120.40474 1784.22232 -2004.90812 -5215.38183 6372.35880 -6329.10427
## 746 747 748 749 750 751
## 22654.46475 7742.18401 -2041.79493 -7375.13637 23345.44757 -4398.92879
## 752 753 754 755 756 757
## 1302.83604 -14512.89868 56046.52202 26773.98639 14923.90030 -10820.49365
## 758 759 760 761 762 763
## 10468.20827 7173.89818 5672.02549 -46521.62839 -16221.33191 941.37890
## 764 765 766 767 768 769
## -2545.89448 -3483.98382 122821.12870 19122.89337 43528.06468 22358.51605
## 770 771 772 773 774 775
## 11845.99422 15627.47217 25511.56034 -99034.15516 -6830.02714 -35890.89887
## 776 777 778 779 780 781
## 1727.39820 -1243.90345 3380.20128 -7437.22236 -1459.19165 -1959.93420
## 782 783 784 785 786 787
## 3410.02555 -7176.14308 -2249.78924 3907.01929 2245.71180 -2782.73052
## 788 789 790 791 792 793
## -4068.07959 1722.47836 2833.33943 -76.01328 -6728.21115 -5799.06487
## 794 795 796 797 798 799
## -1158.00849 -1274.63715 -7829.37206 -2358.25346 -3272.72536 -2669.14162
## 800
## 10720.82102
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17242.47 20095.23 24357.55 24074.97 26432.95 23760.37 24478.09 19700.86
## 10 11 12 13 14 15 16 17
## 19436.90 16774.45 17553.44 14276.42 14327.69 14993.64 16693.55 15010.27
## 18 19 20 21 22 23 24 25
## 16046.97 15419.27 22515.61 21597.82 21076.66 22970.37 22295.04 22948.87
## 26 27 28 29 30 31 32 33
## 24798.44 18714.92 20444.46 28297.15 28354.92 28027.20 25651.96 27057.14
## 34 35 36 37 38 39 40 41
## 30907.72 31256.82 32668.76 30174.49 34146.23 37360.60 34410.94 31215.57
## 42 43 44 45 46 47 48 49
## 30061.08 20622.15 28155.62 30596.87 31688.21 38539.72 38033.31 42704.64
## 50 51 52 53 54 55 56 57
## 46949.21 39632.81 34190.62 29203.57 22334.56 28636.89 25210.82 21501.17
## 58 59 60 61 62 63 64 65
## 25918.17 27180.19 27477.78 27890.41 23753.25 40377.04 42224.62 37461.26
## 66 67 68 69 70 71 72 73
## 41676.44 46601.73 57290.80 55300.68 40514.96 38015.93 41039.75 35328.72
## 74 75 76 77 78 79 80 81
## 30772.05 21457.28 24665.03 20582.76 22672.91 17558.45 19577.82 18802.83
## 82 83 84 85 86 87 88 89
## 17828.65 15893.80 17174.08 20789.20 25215.58 26207.38 26232.39 26850.33
## 90 91 92 93 94 95 96 97
## 30983.49 29811.76 30813.07 28873.71 28071.96 28440.83 28848.21 22413.08
## 98 99 100 101 102 103 104 105
## 25434.02 18450.85 17304.74 15341.63 15641.97 16320.69 20802.08 19883.79
## 106 107 108 109 110 111 112 113
## 23401.76 23191.32 24870.72 27753.12 25246.34 21682.85 21958.81 24610.26
## 114 115 116 117 118 119 120 121
## 35496.02 33685.33 35526.41 38530.59 40490.33 38174.23 32985.12 29319.35
## 122 123 124 125 126 127 128 129
## 31406.25 29682.84 30866.71 38481.33 38123.09 37182.80 34040.20 35824.88
## 130 131 132 133 134 135 136 137
## 41233.31 40672.51 31857.02 33124.29 36320.28 32723.91 31108.03 30191.25
## 138 139 140 141 142 143 144 145
## 26741.55 28158.74 27927.92 25609.70 27638.08 26260.15 19849.78 22886.23
## 146 147 148 149 150 151 152 153
## 20708.67 23691.60 24232.65 25826.14 26008.57 27665.96 28969.24 32007.31
## 154 155 156 157 158 159 160 161
## 27468.65 26730.17 24280.66 30186.25 41672.57 39999.55 37359.36 42389.42
## 162 163 164 165 166 167 168 169
## 43800.73 47223.30 42680.39 37978.42 43414.24 59748.19 61964.63 60375.64
## 170 171 172 173 174 175 176 177
## 57195.15 55591.28 58299.12 57321.31 49598.41 52427.42 56177.33 56213.59
## 178 179 180 181 182 183 184 185
## 63356.32 53840.11 50584.72 41375.48 32898.34 36413.44 46536.59 45991.54
## 186 187 188 189 190 191 192 193
## 51955.90 57712.06 68516.05 73809.19 67491.60 67685.41 74769.49 70437.52
## 194 195 196 197 198 199 200 201
## 65951.62 55174.52 49193.04 50620.11 46206.23 38359.79 44797.04 43020.99
## 202 203 204 205 206 207 208 209
## 42658.25 43137.53 49944.77 58851.29 58480.17 60208.34 61867.35 65665.91
## 210 211 212 213 214 215 216 217
## 75150.86 67218.27 55384.34 49982.66 40961.48 37913.28 41033.00 31033.76
## 218 219 220 221 222 223 224 225
## 48040.30 55375.33 56278.99 79088.71 86598.29 88605.42 96211.53 87144.84
## 226 227 228 229 230 231 232 233
## 81131.59 80712.71 77356.17 76516.03 81262.26 82629.18 77053.82 72257.46
## 234 235 236 237 238 239 240 241
## 77922.09 64543.93 56564.95 48499.95 40095.21 44295.26 46452.18 39890.14
## 242 243 244 245 246 247 248 249
## 33557.42 43859.88 38095.59 42038.94 34287.78 32991.12 36653.51 39482.89
## 250 251 252 253 254 255 256 257
## 30294.53 36244.14 40052.94 45257.81 47981.41 47473.00 57793.96 75325.16
## 258 259 260 261 262 263 264 265
## 75139.85 68437.52 69922.23 66135.68 67584.63 61330.19 50583.97 46603.61
## 266 267 268 269 270 271 272 273
## 46813.42 42912.58 51733.83 47999.67 52154.46 50267.37 54342.64 54639.23
## 274 275 276 277 278 279 280 281
## 60668.35 58297.99 67989.76 61902.74 62113.18 60459.04 66213.08 59931.38
## 282 283 284 285 286 287 288 289
## 56488.56 46020.55 44414.18 61679.92 67220.77 67631.02 65043.59 64129.18
## 290 291 292 293 294 295 296 297
## 68137.93 72055.77 53015.35 43098.14 37097.53 47438.99 50687.13 49799.35
## 298 299 300 301 302 303 304 305
## 74042.76 79999.74 80668.70 85290.14 83485.20 78505.78 81979.36 56829.29
## 306 307 308 309 310 311 312 313
## 53083.12 52762.27 46540.34 43744.34 47354.71 39892.39 38612.00 33162.93
## 314 315 316 317 318 319 320 321
## 36954.86 36134.60 39973.54 37954.04 63790.70 61627.65 63254.41 71268.15
## 322 323 324 325 326 327 328 329
## 73660.14 99191.58 97566.08 73349.05 72144.55 70498.66 62434.65 59551.26
## 330 331 332 333 334 335 336 337
## 29440.89 33135.67 33576.06 35900.53 35240.07 41018.37 42096.08 37334.35
## 338 339 340 341 342 343 344 345
## 36547.23 36674.14 31983.94 37994.68 38659.39 38918.34 39795.86 41585.30
## 346 347 348 349 350 351 352 353
## 43410.86 43162.04 36092.84 26642.15 31997.31 30856.08 30445.33 28057.09
## 354 355 356 357 358 359 360 361
## 32745.17 36506.84 40979.23 39164.67 40427.44 42570.10 49979.91 50531.58
## 362 363 364 365 366 367 368 369
## 50733.64 53201.66 50680.12 50123.58 42754.28 39945.54 36114.97 33889.28
## 370 371 372 373 374 375 376 377
## 29939.30 37241.57 39533.03 47434.11 41393.86 40839.94 39374.36 38906.21
## 378 379 380 381 382 383 384 385
## 29755.87 34363.05 27401.01 35636.57 45990.04 49562.53 47832.49 49828.11
## 386 387 388 389 390 391 392 393
## 56060.55 65564.75 58762.64 53219.91 52948.08 60342.25 60866.67 69552.49
## 394 395 396 397 398 399 400 401
## 58636.60 60206.59 59765.82 59248.66 57732.57 56491.68 43224.06 51826.73
## 402 403 404 405 406 407 408 409
## 50825.17 49789.35 56203.34 48731.02 48040.80 46364.16 42032.81 40861.20
## 410 411 412 413 414 415 416 417
## 38921.22 33003.63 40892.08 43824.24 38485.96 33564.43 48465.69 52318.13
## 418 419 420 421 422 423 424 425
## 56255.23 48709.39 45025.37 43698.70 47292.32 35688.34 35417.00 29664.34
## 426 427 428 429 430 431 432 433
## 35265.33 43609.55 50515.45 47274.44 44337.28 41256.32 41144.28 37615.06
## 434 435 436 437 438 439 440 441
## 33745.73 30974.25 32553.49 34406.56 32407.19 37283.08 43501.02 40246.88
## 442 443 444 445 446 447 448 449
## 39952.29 42964.72 40846.57 44844.06 40081.45 31095.78 29930.92 41310.59
## 450 451 452 453 454 455 456 457
## 40991.49 46654.00 42281.39 42631.75 44255.50 47981.16 37835.13 42694.14
## 458 459 460 461 462 463 464 465
## 38108.21 45692.45 49219.54 51845.36 48568.34 50913.82 51115.76 52866.30
## 466 467 468 469 470 471 472 473
## 52362.90 55312.56 52635.48 57696.81 50943.21 48549.34 47132.65 43750.22
## 474 475 476 477 478 479 480 481
## 47513.89 54992.21 49407.98 51114.63 45893.01 44268.76 47107.39 36500.34
## 482 483 484 485 486 487 488 489
## 30048.96 31922.04 34624.13 36117.10 37084.90 30712.92 43268.32 49941.89
## 490 491 492 493 494 495 496 497
## 56785.90 51486.75 56329.51 63978.51 67797.57 54026.29 44585.23 42606.24
## 498 499 500 501 502 503 504 505
## 42930.09 43722.84 38198.49 40602.24 45914.39 51607.41 52318.51 52429.29
## 506 507 508 509 510 511 512 513
## 46118.58 47461.25 43712.96 46473.94 46138.96 39842.13 40975.48 40148.97
## 514 515 516 517 518 519 520 521
## 41256.82 43902.14 36727.91 31997.81 55935.38 64112.43 67772.69 61138.50
## 522 523 524 525 526 527 528 529
## 62480.67 76093.53 83084.20 57984.27 52844.05 49531.77 53919.51 53424.85
## 530 531 532 533 534 535 536 537
## 43588.79 48590.60 61276.42 55786.34 59190.77 63186.51 60233.22 55254.05
## 538 539 540 541 542 543 544 545
## 48703.26 47356.47 55309.44 55059.36 47604.54 49832.61 49658.93 50354.15
## 546 547 548 549 550 551 552 553
## 40984.48 32803.94 37154.42 45287.20 45083.76 46793.04 40790.92 49821.86
## 554 555 556 557 558 559 560 561
## 50979.47 40738.28 50298.26 58176.71 57540.38 61145.25 56904.06 68687.97
## 562 563 564 565 566 567 568 569
## 85409.80 75481.09 64021.90 68449.90 66570.57 67759.56 59312.81 43270.07
## 570 571 572 573 574 575 576 577
## 50292.63 56209.47 57394.59 59474.23 60122.56 57243.29 69514.23 58866.54
## 578 579 580 581 582 583 584 585
## 52577.09 60193.71 61700.68 54781.02 61059.98 56627.35 53666.05 67263.03
## 586 587 588 589 590 591 592 593
## 52662.74 60021.53 59112.12 52821.91 52125.70 52402.28 43099.77 45902.14
## 594 595 596 597 598 599 600 601
## 40517.96 44772.16 53552.77 46870.81 52741.39 55123.88 60804.15 56954.70
## 602 603 604 605 606 607 608 609
## 61777.95 53329.95 55213.66 55991.53 58304.24 58879.42 58419.28 52585.22
## 610 611 612 613 614 615 616 617
## 59660.79 57717.06 54808.53 51508.13 44457.19 55990.15 59885.24 50802.79
## 618 619 620 621 622 623 624 625
## 61225.28 65418.46 58895.43 81166.73 66260.22 58574.45 60581.33 55925.51
## 626 627 628 629 630 631 632 633
## 46241.87 56970.33 37488.40 37348.35 46935.46 57438.73 55479.49 84264.94
## 634 635 636 637 638 639 640 641
## 74431.88 76636.95 78277.46 72993.44 65698.42 62326.12 50207.35 48572.47
## 642 643 644 645 646 647 648 649
## 47461.87 45941.15 44322.65 47004.73 51631.17 66631.33 81083.83 78201.56
## 650 651 652 653 654 655 656 657
## 79107.09 84990.04 98460.98 93209.35 63421.34 60868.17 57816.59 58803.15
## 658 659 660 661 662 663 664 665
## 55237.17 45631.18 48020.17 52346.39 51537.02 63121.37 62998.83 63288.92
## 666 667 668 669 670 671 672 673
## 51721.82 53084.00 54104.44 49434.11 43402.11 46442.93 44037.06 47530.64
## 674 675 676 677 678 679 680 681
## 45278.69 38102.34 32749.92 32747.42 35501.78 40243.00 42507.83 40508.46
## 682 683 684 685 686 687 688 689
## 40532.09 40981.73 35313.10 41654.82 41151.29 41451.13 43450.50 54179.09
## 690 691 692 693 694 695 696 697
## 62652.85 70717.73 59993.77 55993.78 52870.05 58034.16 48308.14 41994.92
## 698 699 700 701 702 703 704 705
## 35878.77 32591.50 31068.53 36585.87 34846.08 35520.03 41464.51 70179.69
## 706 707 708 709 710 711 712 713
## 76350.23 93931.45 90243.55 92843.86 107898.17 106736.55 84054.50 84402.49
## 714 715 716 717 718 719 720 721
## 75724.04 72821.13 70793.75 53490.00 48879.44 52378.28 49878.25 38972.36
## 722 723 724 725 726 727 728 729
## 44551.09 40815.18 43064.63 40935.72 31625.07 35583.43 36209.87 29833.14
## 730 731 732 733 734 735 736 737
## 47935.65 50187.60 48217.73 53882.49 46274.50 46549.59 54546.33 40971.86
## 738 739 740 741 742 743 744 745
## 37376.99 45894.51 42662.88 44168.35 46942.34 46053.81 42466.07 49468.25
## 746 747 748 749 750 751 752 753
## 44479.82 65482.10 70812.51 66914.42 58834.41 78651.07 71712.16 70629.33
## 754 755 756 757 758 759 760 761
## 55838.48 104651.16 121754.10 126351.78 107842.65 110275.53 109521.55 107547.06
## 762 763 764 765 766 767 768 769
## 60135.19 45157.91 47070.75 45692.70 43665.44 152442.39 156887.65 182139.63
## 770 771 772 773 774 775 776 777
## 185712.86 179639.10 177632.73 184527.87 81551.60 72123.04 38434.32 41873.76
## 778 779 780 781 782 783 784 785
## 42283.51 46689.51 41077.76 41398.36 41240.69 45802.86 40530.22 40227.12
## 786 787 788 789 790 791 792 793
## 45350.72 48381.16 46632.37 43976.66 46720.52 50094.44 50501.07 45034.49
## 794 795 796 797 798 799 800
## 41063.01 41649.07 42059.94 36682.40 36764.30 36035.57 35926.04
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8126
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 4.878707 0.7459174 3.757248
## t2* 2601.669146 167.0204055 877.681266
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 1.071748 4.841002 12.85602
## 2 lag_depvar 1578.807680 2641.123080 4415.57612Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03")))
# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
Data = as.numeric(mstl_data_autplt[, "Data"]),
Trend = as.numeric(mstl_data_autplt[, "Trend"]),
Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)
# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")
# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
facet_wrap(~ Component, scales = "free_y", ncol = 1) +
theme(strip.text = element_text(size = 12),
axis.text.x = element_text(angle = 90, hjust = 1))
library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Dec 30 00:51:50 2024
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## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_24<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2024",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("202",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_24 %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2024","2023","2022","2021","2020"))| Item | 2024 | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|---|
| Agua | 6.190545 | 5.195333 | 5.410333 | 5.849167 | 6.677250 |
| Comida | 329.464273 | 366.009167 | 312.386750 | 317.896583 | 342.723233 |
| Comunicaciones | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| Electricidad | 88.688727 | 38.104750 | 47.072333 | 29.523000 | 43.777483 |
| Enceres | 26.169818 | 18.259750 | 24.219750 | 14.801167 | 24.056333 |
| Farmacia | 0.000000 | 10.704083 | 2.835000 | 13.996083 | 8.314383 |
| Gas/Bencina | 48.319273 | 42.636000 | 45.575000 | 13.583667 | 32.669133 |
| Diosi | 34.869000 | 55.804250 | 31.180667 | 52.687833 | 42.022733 |
| donaciones/regalos | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| Electrodomésticos/ Mantención casa | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| VTR | 17.992727 | 12.829167 | 25.156667 | 19.086917 | 18.902633 |
| Netflix | 1.517909 | 8.713833 | 7.151583 | 7.028750 | 6.506567 |
| Otros | 74.454545 | 5.481667 | 5.000000 | 0.000000 | 16.537733 |
| Total | 627.666818 | 563.738000 | 505.988083 | 474.453167 | 542.187483 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
tryCatch(uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf24 <-uf24[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf24 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf24)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 47 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:2557, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)La proyección de la UF a 298 días más 2024-12-31 00:04:58 sería de: 38.604 pesos// Percentil 95% más alto proyectado: 41.515,44
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 38416.82 | 38416.67 |
| Lo.80 | 38418.31 | 38418.00 |
| Point.Forecast | 38603.86 | 39152.75 |
| Hi.80 | 40264.77 | 44003.75 |
| Hi.95 | 41172.74 | 46571.71 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(0,1,2)
##
## Coefficients:
## ma1 ma2
## -0.5610 -0.3022
## s.e. 0.1113 0.1114
##
## sigma^2 = 38132: log likelihood = -461.44
## AIC=928.88 AICc=929.25 BIC=935.58paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(0,0,1) errors
##
## Coefficients:
## ma1 xreg
## 0.3406 32.7856
## s.e. 0.1029 0.8890
##
## sigma^2 = 33275: log likelihood = -462.81
## AIC=931.63 AICc=931.99 BIC=938.37forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 969.7588 | 743.6487 | 776.0567 |
| Lo.80 | 1100.4934 | 888.3288 | 881.5743 |
| Point.Forecast | 1347.4570 | 1161.6360 | 1121.5821 |
| Hi.80 | 1594.4205 | 1455.6163 | 1446.9683 |
| Hi.95 | 1725.1551 | 1611.2400 | 1655.8181 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CausalImpact_1.3.0 bsts_0.9.10 BoomSpikeSlab_1.2.6
## [4] Boom_0.9.15 scales_1.3.0 ggiraph_0.8.9
## [7] tidytext_0.4.1 DT_0.32 janitor_2.2.0
## [10] autoplotly_0.1.4 rvest_1.0.4 plotly_4.10.4
## [13] xts_0.13.2 forecast_8.21.1 wordcloud_2.6
## [16] RColorBrewer_1.1-3 SnowballC_0.7.1 tm_0.7-11
## [19] NLP_0.2-1 tsibble_1.1.4 lubridate_1.9.3
## [22] forcats_1.0.0 dplyr_1.1.4 purrr_1.0.2
## [25] tidyr_1.3.1 tibble_3.2.1 tidyverse_2.0.0
## [28] gsynth_1.2.1 lattice_0.20-45 GGally_2.2.1
## [31] ggplot2_3.5.0 gridExtra_2.3 plotrix_3.8-4
## [34] sparklyr_1.8.4 httr_1.4.7 readxl_1.4.3
## [37] zoo_1.8-12 stringr_1.5.1 stringi_1.8.3
## [40] data.table_1.15.0 reshape2_1.4.4 fUnitRoots_4021.80
## [43] plyr_1.8.9 readr_2.1.5
##
## loaded via a namespace (and not attached):
## [1] uuid_1.2-0 systemfonts_1.0.5 selectr_0.4-2
## [4] lazyeval_0.2.2 websocket_1.4.1 crosstalk_1.2.1
## [7] listenv_0.9.1 digest_0.6.34 foreach_1.5.2
## [10] htmltools_0.5.7 fansi_1.0.6 ggfortify_0.4.16
## [13] magrittr_2.0.3 doParallel_1.0.17 tzdb_0.4.0
## [16] globals_0.16.2 vroom_1.6.5 sandwich_3.1-0
## [19] askpass_1.2.0 timechange_0.3.0 anytime_0.3.9
## [22] tseries_0.10-55 colorspace_2.1-0 xfun_0.42
## [25] crayon_1.5.2 jsonlite_1.8.8 iterators_1.0.14
## [28] glue_1.7.0 gtable_0.3.4 car_3.1-2
## [31] quantmod_0.4.26 abind_1.4-5 mvtnorm_1.2-4
## [34] DBI_1.2.2 rngtools_1.5.2 Rcpp_1.0.12
## [37] lfe_2.9-0 viridisLite_0.4.2 xtable_1.8-4
## [40] bit_4.0.5 Formula_1.2-5 htmlwidgets_1.6.4
## [43] timeSeries_4032.109 gplots_3.1.3.1 ellipsis_0.3.2
## [46] spatial_7.3-14 farver_2.1.1 pkgconfig_2.0.3
## [49] nnet_7.3-16 sass_0.4.8 dbplyr_2.4.0
## [52] chromote_0.2.0 utf8_1.2.4 labeling_0.4.3
## [55] tidyselect_1.2.0 rlang_1.1.3 later_1.3.2
## [58] munsell_0.5.0 cellranger_1.1.0 tools_4.1.2
## [61] cachem_1.0.8 cli_3.6.2 generics_0.1.3
## [64] evaluate_0.23 fastmap_1.1.1 yaml_2.3.8
## [67] processx_3.8.3 knitr_1.45 bit64_4.0.5
## [70] caTools_1.18.2 future_1.33.1 nlme_3.1-153
## [73] doRNG_1.8.6 slam_0.1-50 xml2_1.3.6
## [76] tokenizers_0.3.0 compiler_4.1.2 rstudioapi_0.15.0
## [79] curl_5.2.0 bslib_0.6.1 highr_0.10
## [82] ps_1.7.6 fBasics_4032.96 Matrix_1.6-5
## [85] its.analysis_1.6.0 urca_1.3-3 vctrs_0.6.5
## [88] pillar_1.9.0 lifecycle_1.0.4 lmtest_0.9-40
## [91] jquerylib_0.1.4 bitops_1.0-7 R6_2.5.1
## [94] promises_1.2.1 KernSmooth_2.23-20 janeaustenr_1.0.0
## [97] parallelly_1.37.0 codetools_0.2-18 ggstats_0.5.1
## [100] assertthat_0.2.1 boot_1.3-28 gtools_3.9.5
## [103] MASS_7.3-54 openssl_2.1.1 withr_3.0.0
## [106] fracdiff_1.5-3 parallel_4.1.2 hms_1.1.3
## [109] quadprog_1.5-8 timeDate_4032.109 rmarkdown_2.25
## [112] snakecase_0.11.1 carData_3.0-5 TTR_0.24.4#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))